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Related Experiment Videos

Comparative deep learning approaches for bean leaf disease recognition.

Kollipara Anirudh1, Darbha Srujan1, Aditya Sai1

  • 1School of Electrical Engineering, Vellore Institute of Technology, Chennai, India.

Frontiers in Plant Science
|May 15, 2026
PubMed
Summary

Related Concept Videos

Light Acquisition02:16

Light Acquisition

In order to produce glucose, plants need to capture sufficient light energy. Many modern plants have evolved leaves specialized for light acquisition. Leaves can be only millimeters in width or tens of meters wide, depending on the environment. Due to competition for sunlight, evolution has driven the evolution of increasingly larger leaves and taller plants, to avoid shading by their neighbors with contaminant elaboration of root architecture and mechanisms to transport water and nutrients.

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Automated plant disease identification using deep learning shows promising results. ResNet18 achieved 99% accuracy in identifying bean leaf diseases, offering an efficient solution for precision agriculture.

Area of Science:

  • Agricultural Science
  • Computer Science
  • Machine Learning

Background:

  • Plant diseases significantly threaten global food security by reducing crop yields.
  • Manual plant leaf inspection is inefficient, subjective, and labor-intensive.
  • Deep learning offers scalable, automated solutions for image-based plant disease identification.

Purpose of the Study:

  • To evaluate deep learning models for automated bean leaf disease identification.
  • To compare the performance of Convolutional Neural Network (CNN), ResNet18, and Vision Transformer (ViT).
  • To establish a benchmarking framework for bean leaf disease detection.

Main Methods:

  • Trained and evaluated three deep learning architectures: CNN, ResNet18, and Vision Transformer (ViT).
Keywords:
ResNet18bean leaf disease detectioncomputer visionconvolutional neural network (CNN)deep learningimage classificationimage recognitionprecision agriculture

Related Experiment Videos

  • Utilized the Augmented iBean dataset with classes for angular leaf spot, bean rust, and healthy leaves.
  • Assessed model performance using accuracy, precision, Receiver Operating Characteristic (ROC) curves, and confusion matrices.

    • Main Results:

    • ResNet18 outperformed CNN and Vision Transformer models in accuracy and precision.
    • ResNet18 achieved 99% accuracy and 99.01% precision with high computational efficiency.
    • Confusion matrix and ROC analysis confirmed ResNet18's superior classification capabilities.

    Conclusions:

    • ResNet18 provides an optimal balance between accuracy and computational efficiency for bean leaf disease identification.
    • The study establishes a standard benchmarking framework for automated plant disease detection.
    • ResNet18 is suitable for real-time deployment in precision agriculture for early disease detection and crop management.